# Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity

arXiv: https://arxiv.org/abs/2005.14635 · HTML: https://mlsanity.com/paper/2005.14635/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 21 of 21)

- [Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics](https://mlsanity.com/paper/2306.06108/) (2023)
- [GuiltyWalker: Distance to illicit nodes in the Bitcoin network](https://mlsanity.com/paper/2102.05373/) (2021)
- [Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin](https://mlsanity.com/paper/2203.10465/) (2022)
- [Effective Generative AI: The Human-Algorithm Centaur](https://mlsanity.com/paper/2406.10942/) (2024)
- [Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs](https://mlsanity.com/paper/2112.07508/) (2021)
- [Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation](https://mlsanity.com/paper/2405.19383/) (2024)
- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
- [Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics](https://mlsanity.com/paper/2206.04803/) (2022)
- [Finding Money Launderers Using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2307.13499/) (2023)
- [Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis](https://mlsanity.com/paper/2401.03530/) (2024)
- [Detecting Malicious Accounts showing Adversarial Behavior in Permissionless Blockchains](https://mlsanity.com/paper/2101.11915/) (2021)
- [Use of Federated Learning and Blockchain towards Securing Financial Services](https://mlsanity.com/paper/2303.12944/) (2023)
- [A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0](https://mlsanity.com/paper/2311.13731/) (2023)
- [New Online Communities: Graph Deep Learning on Anonymous Voting Networks to Identify Sybils in Polycentric Governance](https://mlsanity.com/paper/2311.17929/) (2023)
- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain](https://mlsanity.com/paper/2306.07974/) (2023)
- [Temporal and Geographical Analysis of Real Economic Activities in the Bitcoin Blockchain](https://mlsanity.com/paper/2307.08616/) (2023)
- [The GANfather: Controllable generation of malicious activity to improve defence systems](https://mlsanity.com/paper/2307.13787/) (2023)
- [Beyond Static Datasets: A Behavior-Driven Entity-Specific Simulation to Overcome Data Scarcity and Train Effective Crypto Anti-Money Laundering Models](https://mlsanity.com/paper/2501.00757/) (2025)
- [Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection](https://mlsanity.com/paper/2510.05676/) (2025)
- [Do Transaction-Level and Actor-Level AML Queues Agree? An Empirical Evaluation of Granularity Effects on the Elliptic++ Graph](https://mlsanity.com/paper/2604.23494/) (2026)

## Abstract (arXiv)

Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking) harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.